Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life.However,the enormous and complex nature of this content makes extracting valuable insi...Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life.However,the enormous and complex nature of this content makes extracting valuable insights challenging.Long document summarization emerges as a pivotal technique in this context,serving to distill extensive texts into concise and comprehensible summaries.This paper presents a novel three-stage pipeline for effective long document summarization.The proposed approach combines unsupervised and supervised learning techniques,efficiently handling large document sets while requiring minimal computational resources.Our methodology introduces a unique process for forming semantic chunks through spectral dynamic segmentation,effectively reducing redundancy and repetitiveness in the summarization process.Contrary to previous methods,our approach aligns each semantic chunk with the entire summary paragraph,allowing the abstractive summarization model to process documents without truncation and enabling the summarization model to deduce missing information from other chunks.To enhance the summary generation,we utilize a sophisticated rewrite model based on Bidirectional and Auto-Regressive Transformers(BART),rearranging and reformulating summary constructs to improve their fluidity and coherence.Empirical studies conducted on the long documents from the Webis-TLDR-17 dataset demonstrate that our approach significantly enhances the efficiency of abstractive summarization transformers.The contributions of this paper thus offer significant advancements in the field of long document summarization,providing a novel and effective methodology for summarizing extensive texts in the context of social media.展开更多
SOA is built upon and evolving from older concepts of distributed computing and modular programming, OWL-S plays a key role in describing behaviors of web services, which are the essential of the SOA software. Althoug...SOA is built upon and evolving from older concepts of distributed computing and modular programming, OWL-S plays a key role in describing behaviors of web services, which are the essential of the SOA software. Although OWL-S has given semantics to concepts by ontology technology, it gives no semantics to control-flow and data-flow. This paper presents a formal semantics framework for OWL-S sub-set, including its abstraction, syntax, static and dynamic seman-tics by rewrite logic. Details of a consistent transformation from OWL-S SOS of control-flow to corresponding rules and equations, and dataflow semantics including “Precondition”, “Result” and “Binding” etc. are explained. This paper provides a possibility for formal verification and reliability evaluation of software based on SOA.展开更多
文摘Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life.However,the enormous and complex nature of this content makes extracting valuable insights challenging.Long document summarization emerges as a pivotal technique in this context,serving to distill extensive texts into concise and comprehensible summaries.This paper presents a novel three-stage pipeline for effective long document summarization.The proposed approach combines unsupervised and supervised learning techniques,efficiently handling large document sets while requiring minimal computational resources.Our methodology introduces a unique process for forming semantic chunks through spectral dynamic segmentation,effectively reducing redundancy and repetitiveness in the summarization process.Contrary to previous methods,our approach aligns each semantic chunk with the entire summary paragraph,allowing the abstractive summarization model to process documents without truncation and enabling the summarization model to deduce missing information from other chunks.To enhance the summary generation,we utilize a sophisticated rewrite model based on Bidirectional and Auto-Regressive Transformers(BART),rearranging and reformulating summary constructs to improve their fluidity and coherence.Empirical studies conducted on the long documents from the Webis-TLDR-17 dataset demonstrate that our approach significantly enhances the efficiency of abstractive summarization transformers.The contributions of this paper thus offer significant advancements in the field of long document summarization,providing a novel and effective methodology for summarizing extensive texts in the context of social media.
文摘SOA is built upon and evolving from older concepts of distributed computing and modular programming, OWL-S plays a key role in describing behaviors of web services, which are the essential of the SOA software. Although OWL-S has given semantics to concepts by ontology technology, it gives no semantics to control-flow and data-flow. This paper presents a formal semantics framework for OWL-S sub-set, including its abstraction, syntax, static and dynamic seman-tics by rewrite logic. Details of a consistent transformation from OWL-S SOS of control-flow to corresponding rules and equations, and dataflow semantics including “Precondition”, “Result” and “Binding” etc. are explained. This paper provides a possibility for formal verification and reliability evaluation of software based on SOA.
